目标检测——常用评估指标含义及代码

一、简介

  • 目标检测是一个分类和回归都有的一个任务。

  • 通过混淆矩阵(TP, TN, FP, FN),可以计算出 Precision ( P ), Recall ( R ), Accuracy, F1-Score;

  • IOU 预测的 bbox 和 GT box的交并比.

  • P-R曲线: P和R越高越好,但一般是矛盾的,PR曲线下方的面积AUC(Area Under Curve) 越大说明越好。目标检测中的P-R通过IOU的阈值判断TP, TN, FP, FN,进而去计算。

  • AP(Average Precision): 针对单一类别的,P-R曲线下面的面积(需要对PR曲线平滑处理,即取每个Recall值的时候,选择对应点的Precision的右侧最大的Precision值)。

  • A P I o U = . 50 AP^{IoU=.50} APIoU=.50 : Pascal VOC,计算混淆矩阵、P-R的 IOU 阈值使用的是0.5

  • A P I o U = . 75 AP^{IoU=.75} APIoU=.75 : Pascal VOC,计算混淆矩阵、P-R的 IOU 阈值使用的是0.75,比较严格的评价指标

  • AP: I O U = . 50 : . 05 : . 95 IOU=.50:.05:.95 IOU=.50:.05:.95,IOU阈值从0.5—0.95,按照间隔为0.05取10个IOU阈值的值求得的AP的均值。目前使用比较广泛的一种。

  • AP(根据像素尺寸大小分别计算AP): 小目标: 3 2 2 32^2 322 ; 中目标: 3 2 2 − 9 6 2 32^2-96^2 322962;大目标: 9 6 2 96^2 962

  • AP(根据每张图设定的检测目标个数分别计算AP): 最大目标个数:1,10,100等

  • mAP: 针对所有类别,即 (所有类别的AP和) / (类别总数)


二、代码

1. 计算AP:


def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()):
    """计算每个类别的ap
    tp:根据iou阈值计算的true positive, ndarray, [n, 10], 
        10表示range[0.5, 0.95],间隔0.05取一个iou阈值,预测与标签超过这个iou阈值才为tp
    conf:置信度,ndarray, [n, 1]
    pred_cls:预测类别,ndarray, [n, 1]
    plot:是否画[email protected]的PR曲线
    """
    """ Compute the average precision, given the recall and precision curves.
    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
    # Arguments
        tp:  True positives (nparray, nx1 or nx10).
        conf:  Objectness value from 0-1 (nparray).
        pred_cls:  Predicted object classes (nparray).
        target_cls:  True object classes (nparray).
        plot:  Plot precision-recall curve at [email protected]
        save_dir:  Plot save directory
    # Returns
        The average precision as computed in py-faster-rcnn.
    """

    # Sort by objectness
    # 将tp,conf,pred_cls按照置信度从大到小排序
    i = np.argsort(-conf)
    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]

    # Find unique classes
    # 将target_cls去重,获得类别
    unique_classes = np.unique(target_cls)
    # 获得类别数
    nc = unique_classes.shape[0]  # number of classes, number of detections

    # Create Precision-Recall curve and compute AP for each class
    # 初始化坐标x,y
    px, py = np.linspace(0, 1, 1000), []  # for plotting
    # 初始化指标,ap,precision,recall
    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
    # 对每个类别处理
    for ci, c in enumerate(unique_classes):
        # 选取类别为c的索引
        i = pred_cls == c
        # c类别标签的数量
        n_l = (target_cls == c).sum()  # number of labels
        # c类别预测的数量
        n_p = i.sum()  # number of predictions

        if n_p == 0 or n_l == 0:
            continue
        else:
            # Accumulate FPs and TPs
            # 累计计算fp,tp
            fpc = (1 - tp[i]).cumsum(0)
            tpc = tp[i].cumsum(0)

            # Recall
            # 计算recall
            recall = tpc / (n_l + 1e-16)  # recall curve
            # 插值,方便绘制基于iou_thres=0.5的召回曲线
            r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases

            # Precision
            # 计算precision
            precision = tpc / (tpc + fpc)  # precision curve
            # 插值,方便绘制基于iou_thres=0.5的准确率曲线
            p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score

            # AP from recall-precision curve
            # 根据precision与recall计算ap
            for j in range(tp.shape[1]):
                ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
                if plot and j == 0:
                    py.append(np.interp(px, mrec, mpre))  # precision at [email protected]

    # Compute F1 (harmonic mean of precision and recall)
    # 根据precision与recall计算f1值
    f1 = 2 * p * r / (p + r + 1e-16)
    # 画PR曲线,F1曲线,Precision, recall曲线(后三个的横坐标x为置信度)
    if plot:
        plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)
        plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')
        plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')
        plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')

    i = f1.mean(0).argmax()  # max F1 index
    return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32')


def compute_ap(recall, precision):
    """根据precision与recall计算ap, 计算PR曲线下的面积"""
    """ Compute the average precision, given the recall and precision curves
    # Arguments
        recall:    The recall curve (list)
        precision: The precision curve (list)
    # Returns
        Average precision, precision curve, recall curve
    """

    # Append sentinel values to beginning and end
    mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01]))
    mpre = np.concatenate(([1.], precision, [0.]))

    # Compute the precision envelope
    # np.maximum.accumulate 计算数组的累计最大值
    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))

    # Integrate area under curve
    method = 'interp'  # methods: 'continuous', 'interp'
    if method == 'interp':
        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
        # np.trapz求积分, 求得PR曲线下的面积
        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
    else:  # 'continuous'
        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve

    return ap, mpre, mrec

ps:本博客仅供自己复习理解,不具其他人可参考,本博客参考了大量的优质资源,侵删。

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